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» Unsupervised Learning of Invariant Features Using Video
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CVPR
2010
IEEE
13 years 11 months ago
Learning 3D Action Models from a few 2D videos for View Invariant Action Recognition
Most existing approaches for learning action models work by extracting suitable low-level features and then training appropriate classifiers. Such approaches require large amount...
Pradeep Natarajan, Vivek Singh, Ram Nevatia
MM
2003
ACM
241views Multimedia» more  MM 2003»
13 years 11 months ago
Invariance in motion analysis of videos
In this paper, we propose an approach that retrieves motion of objects from the videos based on the dynamic time warping of view invariant characteristics. The motion is represent...
Cen Rao, Mubarak Shah, Tanveer Fathima Syeda-Mahmo...
MIR
2006
ACM
267views Multimedia» more  MIR 2006»
13 years 11 months ago
Matching slides to presentation videos using SIFT and scene background matching
We present a general approach for automatically matching electronic slides to videos of corresponding presentations for use in distance learning and video proceedings of conferenc...
Quanfu Fan, Kobus Barnard, Arnon Amir, Alon Efrat,...
ICPR
2008
IEEE
14 years 7 months ago
Unsupervised image embedding using nonparametric statistics
Embedding images into a low dimensional space has a wide range of applications: visualization, clustering, and pre-processing for supervised learning. Traditional dimension reduct...
Guobiao Mei, Christian R. Shelton
ECCV
2010
Springer
13 years 11 months ago
Convolutional learning of spatio-temporal features
Abstract. We address the problem of learning good features for understanding video data. We introduce a model that learns latent representations of image sequences from pairs of su...